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Differential difference equations involving noncentral chi-square density and distribution functions for solving minimization problem in selection from multivariate normal populations
Interplanetary distributions from a sample of 20,000 radar meteor observations are presented. These distributions are freed from all known selection effects with the exception of a possible bias against fragmenting meteors which has not yet been adequately assessed. These data thus represent the largest and most accurate collection of radar meteor distributions. Both general average distribution and the distribution of meteor streams with their comet and asteroid associations are presented. Sporadic space density and space density of meteor streams are also included.
We present a sample of luminous red sequence galaxies as the basis for a study of the large-scale structure in the fourth data release of the Kilo-Degree Survey. The selected galaxies are defined by a red sequence template, in the form of a data-driven model of the colour-magnitude relation conditioned on redshift. In this work, the red sequence template was built using the broad-band optical+near infrared photometry of KiDS-VIKING and the overlapping spectroscopic data sets. The selection process involved estimating the red sequence redshifts, assessing the purity of the sample and estimating the underlying redshift distributions of redshift bins. After performing the selection, we mitigated the impact of survey properties on the observed number density of galaxies by assigning photometric weights to the galaxies. We measured the angular two-point correlation function of the red galaxies in four redshift bins and constrain the large-scale bias of our red sequence sample assuming a fixed ΛCDM cosmology. We find consistent linear biases for two luminosity-threshold samples (‘dense’ and ‘luminous’). We find that our constraints are well characterised by the passive evolution model.
In view of the discrepancies in published crater frequency data, effects modifying production size distributions of impact craters are discussed. The resulting criteria are applied to the determination of the size distributions of unmodified impact crater populations in selected lunar regions of different ages. The measured cumulative crater frequencies are used to obtain a general calibration size distribution curve by a normalization procedure. Deviations of measured size distributions from the calibration distribution are strongly suggestive of the existence of processes having modified the primary impact crater population.
The Goddard Distributed Active Archive Center (DAAC), as an integral part of the Earth Observing System Data and Information System (EOSDIS), is the official source of data for several important earth remote sensing missions. These include the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) launched in August 1997, the Tropical Rainfall Measuring Mission (TRMM) launched in November 1997, and the Moderate Resolution Imaging Spectroradiometer (MODIS) scheduled for launch in mid 1999 as part of the EOS AM-1 instrumentation package. The data generated from these missions supports a host of users in the hydrological, land biosphere and oceanographic research and applications communities. The volume and nature of the data present unique challenges to an Earth science data archive and distribution system such as the DAAC. The DAAC system receives, archives and distributes a large number of standard data products on a daily basis, including data files that have been reprocessed with updated calibration data or improved analytical algorithms. A World Wide Web interface is provided allowing interactive data selection and automatic data subscriptions as distribution options. The DAAC also creates customized and value-added data products, which allow additional user flexibility and reduced data volume. Another significant part of our overall mission is to provide ancillary data support services and archive support for worldwide field campaigns designed to validate the results from the various satellite-derived measurements. In addition to direct data services, accompanying documentation, WWW links to related resources, support for EOSDIS data formats, and informed response to inquiries are routinely provided to users. The current GDAAC WWW search and order system is being restructured to provide users with a simplified, hierarchical access to data. Data Browsers have been developed for several data sets to aid users in ordering data. These Browsers allow users to specify spatial, temporal, and other parameter criteria in searching for and previewing data.
This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.
Two-dimensional porous-well cascade tests of the NACA 65- (l2Al0) 10 and NACA 65-(l2A2I8b)10 blade sections were made at Mach numbers from 0.3 to choking in most cases. Data were obtained at solidities of 1.0 and 1.5 et inlet-air angles of 45 deg. and 60 deg. for both blade sections. With a solid--wall modification to the cascade tunnel, schlieren observations were made of the flow in cascade at a solidity of 1.5 and inlet-air angle of 45 deg. and at a solidity of 1.0 and inlet-air angle of 60 deg. Test results for the NACA 65-(12A10)l0 blade section show t hat the turning angles measured at low speed do not change significantly as the speed increases until the critical Mach number is exceed. Because of increasing separation from the highly cambered trailing-edge region, the turning angles for the NACA 65-(12A2I8b)10 blade section decreased as much as 4 deg. from low speed to critical speed. The high-speed performance of the NACA 65-(12A10)10 and the NACA 65-(12A2I8b)l0 blade sections is largely determined by the passage area distribution. The angle of attack for best operation at high Mach numbers is higher than the design angle of attack selected at low speed to have pressure distributions that are free of peaks.
The paper presents an approach to sensors selection and network topology determination for independent airspace surveillance with maximum outcome and minimum cost using ground based distributed sensing, computing and communication network infrastructure. The selection criteria includes minimum estimation error, maximum airspace coverage, minimum communication time and power consumption while guaranteeing the system observability and providing in-time quality information to a monitoring observer. The developed algorithm uses multi-objective optimization strategy taking into account trade-offs between conflicting objectives and relaxations for in time implementation. It is implemented utilizing graph theoretic tools. The approach is validated in a desktop simulation environment using synthetic sensors data generated for a simulated multi-vehicle flight scenario in the selected regional airspace.
The choice of knot vector has immense influence on the resulting accuracy of a B-spline approximation of a curve. However, despite the significance of this problem and the various solutions that were proposed in the literature, optimizing the number and placement of knots remains a difficult task. This paper presents a novel method for the approximation of a curve by a B-spline of arbitrary order, which automatically determines a knot vector that achieves high approximation quality. Additionally, at the core of our approach is a feature function that characterizes the amount and spatial distribution of geometric details in the input curve by estimating its derivatives. Knots are then selected in such a way as to evenly distribute the feature contents across their intervals. A comparison to the state of the art for a wide variety of curves shows that our method is faster and achieves more accurate reconstruction results, while typically reducing the number of necessary knots.
The statistical occurrence of pulsation or oblique structure about the earth's generally nonuniform bow shock is estimated at selected points by combining a three-dimensional distribution of interplanetary field directions obtained for a six-day solar wind sector with an index of local pulsation geometry. The result, obtained with a pulsation index of 1.6, is a set of distribution patterns showing the dependence of the pulsation index on the field orientation at the selected shock loci for this value of the index.
While lithium metal represents the ultimate high-energy-density battery anode material, its use is limited by dendrite formation and associated safety risks, motivating studies of the solid–electrolyte interphase layer that forms on the lithium, which is key in controlling lithium metal deposition. Dynamic nuclear polarisation enhanced NMR can provide important structural information; however, typical exogenous dynamic nuclear polarisation experiments, in which organic radicals are added to the sample, require cryogenic sample cooling and are not selective for the interface between the metal and the solid–electrolyte interphase. Here we instead exploit the conduction electrons of lithium metal to achieve an order of magnitude hyperpolarisation at room temperature. We enhance the 7 Li, 1 H and 19 F NMR spectra of solid–electrolyte interphase species selectively, revealing their chemical nature and spatial distribution. These experiments pave the way for more ambitious room temperature in situ dynamic nuclear polarisation studies of batteries and the selective enhancement of metal–solid interfaces in a wider range of systems.
Excellent ethylene selectivity in acetylene semi-hydrogenation is often obtained at the expense of activity. To break the activity-selectivity trade-off, precise control and in-depth understanding of the three-dimensional atomic structure of surfacial active sites are crucial. Here, we designed a novel Au@PdCu core–shell nanocatalyst featuring diluted and stretched Pd sites on the ultrathin shell (1.6 nm), which showed excellent reactivity and selectivity, with 100% acetylene conversion and 92.4% ethylene selectivity at 122 °C, and the corresponding activity was 3.3 times higher than that of the PdCu alloy. The atomic three-dimensional decoding for the activity-selectivity balance was revealed by combining pair distribution function (PDF) and reverse Monte Carlo simulation (RMC). The results demonstrate that a large number of active sites with a low coordination number of Pd–Pd pairs and an average 3.25% tensile strain are distributed on the surface of the nanocatalyst, which perform a pivotal function in the simultaneous improvement of hydrogenation activity and ethylene selectivity. Our work not only develops a novel strategy for unlocking the linear scaling relation in heterogeneous catalysis but also provides a paradigm for atomic 3D understanding of lattice strain in core–shell nanocatalysts.
A simple form is presented of the relationships for the inviscid, fully developed structure of lift-generated vortices behind aircraft wings. The method is then extended to arbitrary span-load distributions by inferring guidelines for the selection of rollup centers for the vortex sheet, along with rules for calculating the fully developed structure of the resulting multiple vortices. These techniques yield realistic estimates of the rolled-up structure of vortices produced by a wider variety of span-load distributions than possible with the original form of the theory.
Guerbet coupling followed by etherification is a two step process for conversion of ethanol into distillate range molecules. This involves first alcohol oligomerization, followed by acid-catalyzed dehydration. As we will show in this presentation, ethanol can be converted to higher linear and α-branched alcohols by C-C coupling reactions (ethanol oligomerization) as well as to high molecular weight esters by C-O coupling reactions with Cu/MgxAlOy (CuHT) catalysts. Alcohols may be converted to ethers via bimolecular dehydration in a subsequent step. We have identified more than 160 number of species in the products including alcohols, esters, aldehydes, ketonces, and olefins. Alcohols range from C4 to C10. Both alcohols and esters follow a Schulz-Flory chain growth model. We show the relationship between the catalyst properties (BET surface area, acid and base site count and Cu loading and synthesis method) and the performance in the reactions. We also show that physical mixtures of CuHT and HT can have similar product selectivity of low loading CuHT catalysts. The selectivity towards diesel fuel precursor compounds (hereafter ‘DFPC’) increased with conversion until reaching a plateau at high ethanol conversion (~70%). Alcohol selectivity follows a Schultz-Flory distribution at all studied conversions, and that adsorbed ethanol-derived species may undergo surface oligomerization into 1-butanol and higher alcohols before desorbing in a chain-growth mechanism. Zeolite catalysts convert the C4+ alcohols into C8+ ethers in both batch and continuous flow reactors. Selectivities of up to 80% to C8+ ethers at around 70% conversion using a single pass continuous flow system are able to be achieved. The final product feedstocks obtained from etherification have been used in technoeconomic (TEA) and lifecycle analysis (LCA), which indicate a reduction in greenhouse gas (GHG) emissions of 50% relative to conventional diesel. These final feedstocks are undergoing engine testing to elucidate the physiochemical properties and will be compared to diesel #2 ASTM standards as a reference and will serve as a basis to improve the final bio-diesel blendstocks from etherification.
In this presentation we describe the catalytic approaches for conversion of ethanol into diesel fuel ethers. This involves first alcohol oligomerization, followed by acid-catalyzed dehydration. As we will show in this presentation, ethanol can be converted to higher linear and α-branched alcohols by C-C coupling reactions (ethanol oligomerization) as well as to high molecular weight esters by C-O coupling reactions with Cu/MgxAlOy (CuHT) catalysts. Alcohols may be converted to ethers via bimolecular dehydration in a subsequent step. We have identified more than 160 number of species in the products including alcohols, esters, aldehydes, ketones, and olefins. Alcohols range from C4 to C10. Both alcohols and esters follow a Schulz-Flory chain growth model. We show the relationship between the catalyst properties (BET surface area, acid and base site count and Cu loading and synthesis method) and the performance in the reactions. We also show that physical mixtures of CuHT and HT can have similar product selectivity of low loading CuHT catalysts. The selectivity towards diesel fuel precursor compounds (hereafter ‘DFPC’) increased with conversion until reaching a plateau at high ethanol conversion (~70%). Alcohol selectivity follows a Schultz-Flory distribution at all studied conversions, and that adsorbed ethanol-derived species may undergo surface oligomerization into 1-butanol and higher alcohols before desorbing in a chain-growth mechanism. Zeolite catalysts convert the C4+ alcohols into C8+ ethers in both batch and continuous flow reactors. Selectivities of up to 80% to C8+ ethers at around 70% conversion using a single pass continuous flow system are achieved. The final product feedstocks obtained from etherification have been used in technoeconomic (TEA) and lifecycle analysis (LCA), which indicate a reduction in greenhouse gas (GHG) emissions of 50% relative to conventional diesel and diesel fuel prices that are lower than biodiesel. These final feedstocks are undergoing engine testing to elucidate the physiochemical properties and compare well to diesel #2 ASTM standards.
The Moderate Resolution Imaging Spectroradiometer was launched on the EOS Terra spacecraft in December 1999 and will also fly on EOS Aqua in December 2000. With 36 spectral bands from the visible through thermal infrared and spatial resolution of 250m to 1 kilometer, each MODIS instrument will image the entire Earth surface in 2 days. This paper traces the flow of MODIS data products from the receipt of Level 0 data at the EDOS facility, through the production and quality assurance process to the Distributed Active Archive Centers (DAACs), which ship products to the user community. It describes where to obtain products and plans for reprocessing MODIS products. As most components of the ground system are severely limited in their capacity to distribute MODIS products, it also describes the key characteristics of MODIS products and their metadata that allow a user to optimize their selection of products given anticipate bottlenecks in distribution.
Major system design features of a distributed data management system for the NASA Deep Space Network (DSN) designed for continuous two-way deep space communications are described. The reasons for which the distributed data base utilizing third-generation minicomputers is selected as the optimum approach for the DSN are threefold: (1) with a distributed master data base, valid data is available in real-time to support DSN management activities at each location; (2) data base integrity is the responsibility of local management; and (3) the data acquisition/distribution and processing power of a third-generation computer enables the computer to function successfully as a data handler or as an on-line process controller. The concept of the distributed data base is discussed along with the software, data base integrity, and hardware used. The data analysis/update constraint is examined.
Large-scale cultivation of macroalgae is one of the most promising biofuel sources that could reduce our consumption of fossil fuels and would be particularly economically competitive when grown for the co-production of additional goods such as food and textiles. Current production of biofuel from algal biomass is limited by labor costs and lack of data on potential impacts of cultivation and harvesting on marine and coastal environments as well as a comprehensive characterization of the impact of environmental conditions on macroalgal feedstock and their bioproduct and biofuel yield and quality. Awarded by the United States (U.S.) Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) Macroalgae Research Inspiring Novel Energy Resources (MARINER) program, which seeks to enable the U.S. as a global leader in the production of marine biomass, the Marine Biological Laboratory (MBL) has been developing a test system for tropical seaweed cultivation in the Gulf of Mexico and the Caribbean. An integral step in designing macroalgae cultivation farms is to adequately characterize the hydrodynamic climate at potential growth sites. In regions like the Gulf of Mexico and the Caribbean, which are highly exposed to tropical cyclones, it is critical to not only consider the day-to-day hydrodynamic conditions but to also assess the risk of extreme sea states to ensure the survivability of future macroalgae farms. Under these considerations, the Pacific Northwest National Laboratory (PNNL) is providing modeling support for MBL to inform the design and siting of the farm systems that have been proposed for their ARPA-E MARINER project. This report describes the development of a high-resolution coupled storm surge and wave model to simulate the hydrodynamics and wave climate at proposed macroalgae cultivation sites selected by MBL in Florida and Puerto Rico. Model results, including model validation, water level, current distributions, and sea states, are discussed for both selected sites in Florida and Puerto Rico coast. These simulations provide an accurate insight into the hydrodynamic conditions that a macroalgae farm is likely to experience during its operational lifetime, including current information to support fine-scale hydrodynamic load modeling, risk analysis and system design.